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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

A. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.
B. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.
C. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.
D. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.

Question 2

Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
B. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
C. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
D. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.

Question 3

A teammate has asked you to explain why the team's Claude application is billed for output tokens at a different rate than input tokens. They had assumed the rate was the same for both.
How would you explain the difference?

A. Output tokens are typically billed at a higher rate than input tokens, and cost models for the application should reflect both rates separately.
B. Output tokens are typically billed at a lower rate than input tokens, because output tokens are cheaper to produce than input tokens are to process.
C. Output tokens are not billed at all, because cost is determined entirely by the input tokens sent to the model on each request.
D. Output tokens are typically billed at the same rate as input tokens, and the apparent rate difference is a billing error to report to Anthropic.

Question 4

A Claude application is producing outputs that drift away from the expected JSON format after several conversation turns. The first few turns produce correctly formatted output, but later turns gradually lose structure.
How would you address the drift?

A. Truncate every response to the first few characters, validate that the truncated output matches the expected JSON structure, and log any mismatches for review.
B. Restart the application after every turn and monitor whether the format remains consistent across subsequent interactions.
C. Identify the failure mode as format drift, examine how the conversation context evolves over turns, and address the drift through context management or output validation.
D. Switch to a smaller Claude model and re-test the application to determine whether the drift persists across conversation turns.

Question 5

The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...

A. Adding regression tests to cover the parameter's behavior, then migrating call sites in batches that you validate against regression tests.
B. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.
C. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.
D. Migrating all 40 call sites in a single change near the removal date to ensure the deprecated parameter continues to work as long as possible.

Solutions: